Description
MSCA-PF: Joint application at the University of Granada. Department of Computer Science and Artificial Intelligence.
Professor Daniel Peralta, from the Department of Computer Science and Artificial Intelligence at the University of Granada, welcomes postdoctoral candidates interested in applying for a Marie Skłodowska-Curie Postdoctoral Fellowship (MSCA-PF) in 2026 at this University. Please note that applicants must comply with the Mobility Rule (for more information about the 2026 call, please consult this link).
Brief description of the institution
The University of Granada (UGR), founded in 1531, is one of the largest and most important universities in Spain. With more than 57,000 undergraduate and postgraduate students and almost 7,000 members of staff, the UGR offers 97 undergraduate degrees, 157 master’s degrees (7 of which are double degrees) and 28 doctoral programmes via its 124 departments and nearly 50 centers. Accordingly, the UGR offers one of the most extensive and diverse ranges of higher education programmes in Spain.
The UGR has been awarded with the "Human Resources Excellence in Research (HRS4R)", which reflects the institution’s commitment to continuously improve its human resource policies in line with the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers. The UGR is also internationally renowned for its excellence in diverse research fields and ranked among the top Spanish universities in a variety of ranking criteria, such as national R&D projects, fellowships awarded, publications, and international funding.
The UGR is one of the few Spanish Universities listed in the Shanghai Top 500 ranking - Academic Ranking of World Universities (ARWU). The 2025 edition of the ARWU places the UGR in 301-400th position in the world and as the 3-8 highest ranked University in Spain, reaffirming its position as an institution at the forefront of national and international research. From the perspective of specialist areas in the ARWU rankings, the UGR is outstanding in Mathematics, Artificial Intelligence and Dentistry & Oral Sciences (ranked between 51th-75th position), Computer Science & Engineering, Education and Hospitality & Tourism Management (between 76-100th position), and in the areas of Food Science & Technology and Business Administration (between 101-150th position). A little lower in the ranking, the UGR also stands out in the areas of Earth Sciences, Law, Management, Nursing, Psychology and Statistics, in which the UGR is positioned in the 151-200th position.
Additionally, the UGR counts with 4 researchers at the top of the Highly Cited Researchers (HCR) list, most of them related to the Computer Science and Mathematics scientific areas. It is also well recognised for its presence in the top 200 Universities in Europe at 83th place.
Internationally, the University of Granada is firmly committed to its participation in the calls of the Framework Programme of the European Union. For the duration of the prevoius Framework Programme, Horizon 2020, the UGR obtained a total of 124 projectswith a total funding of more than €30 million. For the current Framework Programme, Horizon Europe, the UGR has obtained 136 projects, so far, with a total funding of more than €38 million.
Brief description of the Centre/Research Group
The research group "Soft Computing and Intelligent Information Systems" (SCI2S) (http://sci2s.ugr.es) was created in 2001 for a set of researchers who work in Soft Computing and had begun to collaborate few years earlier. Throughout these 14 years the group has grown and expanded its areas of work in different fields of Computational Intelligence, Biometrics, Bibliometrics, Data Science and Big Data, etc ... and applied fields such as bioinformatics, information retrieval, image registration, decision making , etc ...
Francisco Herrera is the director of the research group.
The Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI Institute), is a research institute promoted by the universities of Granada (UGR), Jaén (UJA) and Córdoba (UCO).
The DaSCI Institute aims to promote research, innovation and technology transfer in AI to our socioeconomic environment, sharing common objectives, knowledge and infrastructures with the other Andalusian agents. To do this, we have the strength of our researchers and we have designed a strengthening plan with 5 axes: the training of Phd, the promotion of our research areas (data science, computational intelligence and technological applications), internationalization, knowledge transfer, scientific dissemination and professional training in AI.
Project description
Antimicrobial Resistance (AMR) is a primary threat to global public health. Resistance arises through either intrinsic bacterial traits or acquired biological mechanisms. Rapid diagnosis is critical to ensure effective treatment and curb the misuse of antimicrobials.
While traditional culture-based methods can take 72h, Mass Spectrometry (MS) (specifically MALDI-TOF) provides a protein profile in minutes, allowing for rapid AMR detection. Complementary to this, Fourier Transform Infrared Spectroscopy (FT-IR) rapidly quantifies lipids and carbohydrates. Despite their potential, the integration of MS and FT-IR data using deep learning remains an underexplored frontier. This project seeks to bridge this gap, moving beyond single-species analysis toward a universal, multi-modal diagnostic framework.
Project Objectives
- Engineer Fusion-Based Explainable AI (XAI) Models: Design and train deep learning architectures capable of processing dual-input streams from both MS and FT-IR. A core focus will be on explainability, ensuring the models provide "interpretable evidence" for their predictions, which is vital for clinical validation and physician trust.
- Map Biochemical Resistance Fingerprints: Go beyond binary classification to infer specific biological resistance mechanisms. By synthesizing protein data from MS and lipid/carbohydrate data from FT-IR, the project aims to identify the specific biochemical pathways bacteria use to evade treatment.
- Develop Species-Agnostic Predictive Frameworks: Create models that function across multiple bacterial species simultaneously, eliminating the need for time-consuming pre-identification, accelerating the clinical decision-making process and allowing for efficient training on large, heterogeneous datasets.
Research Area
- Information Science and Engineering (ENG)
- Life Sciences (LIFE)
For a correct evaluation of your candidature, please send the documents below to Professor Daniel Peralta (dperalta@ugr.es):
- CV
- Letter of recommendation (optional)
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